Overview

Dataset statistics

Number of variables13
Number of observations77947
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 MiB
Average record size in memory104.0 B

Variable types

NUM12
CAT1

Reproduction

Analysis started2020-11-20 08:57:05.719601
Analysis finished2020-11-20 08:57:56.572204
Duration50.85 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

memfree is highly correlated with ActiveHigh correlation
Active is highly correlated with memfreeHigh correlation
Xmit Data is highly skewed (γ1 = 22.44583675) Skewed
Sekunde has unique values Unique
Xmit Data has 70125 (90.0%) zeros Zeros
Running App ID has 23661 (30.4%) zeros Zeros

Variables

Sekunde
Real number (ℝ≥0)

UNIQUE

Distinct count77947
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38973.0
Minimum0
Maximum77946
Zeros1
Zeros (%)< 0.1%
Memory size609.0 KiB
2020-11-20T09:57:56.671206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3897.3
Q119486.5
median38973
Q358459.5
95-th percentile74048.7
Maximum77946
Range77946
Interquartile range (IQR)38973

Descriptive statistics

Standard deviation22501.50505
Coefficient of variation (CV)0.5773613798
Kurtosis-1.2
Mean38973
Median Absolute Deviation (MAD)19487
Skewness0
Sum3037828431
Variance506317729.7
2020-11-20T09:57:56.893207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
272881< 0.1%
 
88491< 0.1%
 
149941< 0.1%
 
129471< 0.1%
 
27081< 0.1%
 
6611< 0.1%
 
68061< 0.1%
 
47591< 0.1%
 
252411< 0.1%
 
Other values (77937)77937> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
779461< 0.1%
 
779451< 0.1%
 
779441< 0.1%
 
779431< 0.1%
 
779421< 0.1%
 

Active
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count17127
Unique (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9120197.297137799
Minimum392252
Maximum41529616
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:57.169208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum392252
5-th percentile418752
Q1421512
median2111216
Q316356984
95-th percentile41497858.8
Maximum41529616
Range41137364
Interquartile range (IQR)15935472

Descriptive statistics

Standard deviation12042257.49
Coefficient of variation (CV)1.320394406
Kurtosis1.014888216
Mean9120197.297
Median Absolute Deviation (MAD)1692460
Skewness1.424006751
Sum7.108920187e+11
Variance1.450159654e+14
2020-11-20T09:57:57.316207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4186762770.4%
 
4211522450.3%
 
4215722050.3%
 
4216041740.2%
 
4209361700.2%
 
4191161610.2%
 
4187241610.2%
 
4209321580.2%
 
21114081570.2%
 
4211801500.2%
 
Other values (17117)7608997.6%
 
ValueCountFrequency (%) 
39225213< 0.1%
 
3981721< 0.1%
 
40500410< 0.1%
 
4050123< 0.1%
 
4053402< 0.1%
 
ValueCountFrequency (%) 
415296161< 0.1%
 
415273321< 0.1%
 
415271723< 0.1%
 
415271161< 0.1%
 
415265963< 0.1%
 

CPU Cycles
Real number (ℝ≥0)

Distinct count77782
Unique (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1304743199.0354984
Minimum20932169
Maximum2284932883
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:57.555208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20932169
5-th percentile936510773.3
Q11143903428
median1205632679
Q31505417824
95-th percentile1750084673
Maximum2284932883
Range2264000714
Interquartile range (IQR)361514397

Descriptive statistics

Standard deviation275904314.9
Coefficient of variation (CV)0.2114625431
Kurtosis1.222436979
Mean1304743199
Median Absolute Deviation (MAD)136480772
Skewness0.04831912006
Sum1.017008181e+14
Variance7.6123191e+16
2020-11-20T09:57:57.738243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
114330057111< 0.1%
 
205980785311< 0.1%
 
11517844982< 0.1%
 
11518161862< 0.1%
 
11424616082< 0.1%
 
11323132922< 0.1%
 
11476085912< 0.1%
 
11579215742< 0.1%
 
15046579252< 0.1%
 
11430247342< 0.1%
 
Other values (77772)77909> 99.9%
 
ValueCountFrequency (%) 
209321691< 0.1%
 
244056531< 0.1%
 
246897091< 0.1%
 
282819991< 0.1%
 
309842671< 0.1%
 
ValueCountFrequency (%) 
22849328831< 0.1%
 
22819829881< 0.1%
 
22726440841< 0.1%
 
22723847101< 0.1%
 
22693592481< 0.1%
 

Kernel Stack
Real number (ℝ≥0)

Distinct count144
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30409.511078040206
Minimum29696
Maximum32256
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:57.912207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum29696
5-th percentile29840
Q129888
median30720
Q330752
95-th percentile30784
Maximum32256
Range2560
Interquartile range (IQR)864

Descriptive statistics

Standard deviation431.1568448
Coefficient of variation (CV)0.01417835504
Kurtosis-1.415339776
Mean30409.51108
Median Absolute Deviation (MAD)48
Skewness-0.1987945999
Sum2370330160
Variance185896.2248
2020-11-20T09:57:58.063208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
307521405818.0%
 
307361026413.2%
 
307201000612.8%
 
29856944912.1%
 
2984050806.5%
 
3076844835.8%
 
2987236874.7%
 
2998425703.3%
 
3000024413.1%
 
2988820222.6%
 
Other values (134)1388717.8%
 
ValueCountFrequency (%) 
296963< 0.1%
 
2971218< 0.1%
 
297283< 0.1%
 
297602< 0.1%
 
2977612< 0.1%
 
ValueCountFrequency (%) 
322562< 0.1%
 
321923< 0.1%
 
321762< 0.1%
 
321443< 0.1%
 
320961< 0.1%
 

temp s0
Real number (ℝ≥0)

Distinct count24
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57047.378346825404
Minimum50000
Maximum73000
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:58.235207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum50000
5-th percentile52000
Q153000
median57000
Q360000
95-th percentile69000
Maximum73000
Range23000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation4867.621298
Coefficient of variation (CV)0.08532594203
Kurtosis0.3428277897
Mean57047.37835
Median Absolute Deviation (MAD)4000
Skewness0.8614093916
Sum4446672000
Variance23693737.1
2020-11-20T09:57:58.366205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
520001369917.6%
 
590001250916.0%
 
530001240715.9%
 
6000065988.5%
 
5800048926.3%
 
6200039935.1%
 
5100036564.7%
 
5700034814.5%
 
5400032484.2%
 
6100032434.2%
 
Other values (14)1022113.1%
 
ValueCountFrequency (%) 
500004< 0.1%
 
5100036564.7%
 
520001369917.6%
 
530001240715.9%
 
5400032484.2%
 
ValueCountFrequency (%) 
730008< 0.1%
 
720001790.2%
 
710004420.6%
 
700009191.2%
 
6900025573.3%
 

energy s0
Real number (ℝ≥0)

Distinct count77903
Unique (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132151233268.6379
Minimum9405066
Maximum262140658019
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:58.541205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9405066
5-th percentile1.332755176e+10
Q16.587288401e+10
median1.318598552e+11
Q31.990069791e+11
95-th percentile2.499532941e+11
Maximum2.62140658e+11
Range2.62131253e+11
Interquartile range (IQR)1.331340951e+11

Descriptive statistics

Standard deviation7.624111906e+10
Coefficient of variation (CV)0.5769232505
Kurtosis-1.219555988
Mean1.321512333e+11
Median Absolute Deviation (MAD)6.658869654e+10
Skewness-0.0103145408
Sum1.030079218e+16
Variance5.812708235e+21
2020-11-20T09:57:58.674208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
6.096967735e+1011< 0.1%
 
5.904585623e+1011< 0.1%
 
5.78314526e+1011< 0.1%
 
1.030114842e+1111< 0.1%
 
8.501876386e+104< 0.1%
 
1.486431779e+112< 0.1%
 
2.253004815e+111< 0.1%
 
2.020862031e+111< 0.1%
 
2.107383181e+111< 0.1%
 
6.420977330e+101< 0.1%
 
Other values (77893)7789399.9%
 
ValueCountFrequency (%) 
94050661< 0.1%
 
215739191< 0.1%
 
253148761< 0.1%
 
282915531< 0.1%
 
288740101< 0.1%
 
ValueCountFrequency (%) 
2.62140658e+111< 0.1%
 
2.62136684e+111< 0.1%
 
2.621358984e+111< 0.1%
 
2.621339909e+111< 0.1%
 
2.6213347e+111< 0.1%
 

TempS1
Real number (ℝ≥0)

Distinct count26
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55024.01631878071
Minimum51000
Maximum76000
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:58.830207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum51000
5-th percentile52000
Q153000
median54000
Q354000
95-th percentile72000
Maximum76000
Range25000
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation5111.301974
Coefficient of variation (CV)0.09289220082
Kurtosis7.197249006
Mean55024.01632
Median Absolute Deviation (MAD)1000
Skewness2.943320461
Sum4288957000
Variance26125407.87
2020-11-20T09:57:58.953207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
540002978938.2%
 
530002976738.2%
 
5200066978.6%
 
5500037984.9%
 
7300018692.4%
 
7200013231.7%
 
740009641.2%
 
560005100.7%
 
710005010.6%
 
700003820.5%
 
Other values (16)23473.0%
 
ValueCountFrequency (%) 
5100013< 0.1%
 
5200066978.6%
 
530002976738.2%
 
540002978938.2%
 
5500037984.9%
 
ValueCountFrequency (%) 
760001< 0.1%
 
750002010.3%
 
740009641.2%
 
7300018692.4%
 
7200013231.7%
 

energy s1
Real number (ℝ≥0)

Distinct count77903
Unique (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134010689406.99327
Minimum3138969
Maximum262141492368
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:59.155208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3138969
5-th percentile1.413000896e+10
Q17.180097219e+10
median1.366624185e+11
Q31.970947247e+11
95-th percentile2.479026159e+11
Maximum2.621414924e+11
Range2.621383534e+11
Interquartile range (IQR)1.252937525e+11

Descriptive statistics

Standard deviation7.410043282e+10
Coefficient of variation (CV)0.5529441953
Kurtosis-1.144986966
Mean1.340106894e+11
Median Absolute Deviation (MAD)6.249007794e+10
Skewness-0.07539004633
Sum1.044573121e+16
Variance5.490874144e+21
2020-11-20T09:57:59.278208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
574612969911< 0.1%
 
1.722310461e+1111< 0.1%
 
1.697579759e+1111< 0.1%
 
1.771372902e+1111< 0.1%
 
4.415038984e+104< 0.1%
 
3.448389591e+102< 0.1%
 
61503066421< 0.1%
 
3.514860692e+101< 0.1%
 
1.172573528e+111< 0.1%
 
2.609001829e+111< 0.1%
 
Other values (77893)7789399.9%
 
ValueCountFrequency (%) 
31389691< 0.1%
 
64752031< 0.1%
 
102616921< 0.1%
 
195736191< 0.1%
 
220509071< 0.1%
 
ValueCountFrequency (%) 
2.621414924e+111< 0.1%
 
2.621396503e+111< 0.1%
 
2.62137286e+111< 0.1%
 
2.621338241e+111< 0.1%
 
2.621277105e+111< 0.1%
 

memfree
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count28714
Unique (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81754743.32759438
Minimum49268656
Maximum90495376
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:59.578208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum49268656
5-th percentile49293788
Q174494940
median88773272
Q390464616
95-th percentile90484344
Maximum90495376
Range41226720
Interquartile range (IQR)15969676

Descriptive statistics

Standard deviation12069177.84
Coefficient of variation (CV)0.1476266373
Kurtosis1.016315586
Mean81754743.33
Median Absolute Deviation (MAD)1710872
Skewness-1.424468501
Sum6.372536978e+12
Variance1.456650538e+14
2020-11-20T09:57:59.753208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
90473804660.1%
 
90470092560.1%
 
90469712520.1%
 
90473372480.1%
 
90464728470.1%
 
89596784470.1%
 
90467604460.1%
 
90460856460.1%
 
88778620460.1%
 
89993056460.1%
 
Other values (28704)7744799.4%
 
ValueCountFrequency (%) 
492686561< 0.1%
 
492687321< 0.1%
 
492687524< 0.1%
 
492689881< 0.1%
 
492691601< 0.1%
 
ValueCountFrequency (%) 
904953763< 0.1%
 
9049505625< 0.1%
 
904949761< 0.1%
 
904949041< 0.1%
 
904948843< 0.1%
 

Branch Instructions
Real number (ℝ≥0)

Distinct count77694
Unique (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36487909.7326645
Minimum1343612
Maximum201933878
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:57:59.972209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1343612
5-th percentile8739053.3
Q110791997
median35745651
Q353710397.5
95-th percentile77973220.5
Maximum201933878
Range200590266
Interquartile range (IQR)42918400.5

Descriptive statistics

Standard deviation27633320.04
Coefficient of variation (CV)0.757328119
Kurtosis1.323597768
Mean36487909.73
Median Absolute Deviation (MAD)24286232
Skewness0.9955774892
Sum2.8441231e+12
Variance7.636003766e+14
2020-11-20T09:58:00.204271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4846020611< 0.1%
 
86257593< 0.1%
 
113356902< 0.1%
 
756382412< 0.1%
 
86839892< 0.1%
 
99365312< 0.1%
 
481976992< 0.1%
 
545451882< 0.1%
 
645209852< 0.1%
 
519257972< 0.1%
 
Other values (77684)77917> 99.9%
 
ValueCountFrequency (%) 
13436121< 0.1%
 
14144441< 0.1%
 
14640061< 0.1%
 
18894581< 0.1%
 
21087701< 0.1%
 
ValueCountFrequency (%) 
2019338781< 0.1%
 
2001607421< 0.1%
 
1998516061< 0.1%
 
1994485561< 0.1%
 
1974017741< 0.1%
 

Xmit Data
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count1036
Unique (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.14421337575533
Minimum0
Maximum114998
Zeros70125
Zeros (%)90.0%
Memory size609.0 KiB
2020-11-20T09:58:00.417270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile163
Maximum114998
Range114998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3791.106616
Coefficient of variation (CV)15.52814447
Kurtosis540.5416338
Mean244.1442134
Median Absolute Deviation (MAD)0
Skewness22.44583675
Sum19030309
Variance14372489.37
2020-11-20T09:58:00.559271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
07012590.0%
 
1011341.5%
 
275090.7%
 
785070.7%
 
953620.5%
 
443190.4%
 
1122380.3%
 
1972130.3%
 
2142130.3%
 
1462130.3%
 
Other values (1026)41145.3%
 
ValueCountFrequency (%) 
07012590.0%
 
1011341.5%
 
2215< 0.1%
 
275090.7%
 
348< 0.1%
 
ValueCountFrequency (%) 
1149981< 0.1%
 
1111051< 0.1%
 
1106231< 0.1%
 
1072281< 0.1%
 
1064981< 0.1%
 

Running App
Categorical

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size609.0 KiB
None
23661
AMG
12118
Kripke
9052
Quicksilver
8785
PENNANT
8718
Other values (2)
15613
ValueCountFrequency (%) 
None2366130.4%
 
AMG1211815.5%
 
Kripke905211.6%
 
Quicksilver878511.3%
 
PENNANT871811.2%
 
linpack800910.3%
 
LAMMPS76049.8%
 
2020-11-20T09:58:00.765254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length5.704619806
Min length3

Running App ID
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.332828716948696
Minimum0
Maximum6
Zeros23661
Zeros (%)30.4%
Memory size609.0 KiB
2020-11-20T09:58:00.913732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.092847849
Coefficient of variation (CV)0.8971288092
Kurtosis-1.205780872
Mean2.332828717
Median Absolute Deviation (MAD)2
Skewness0.3879968827
Sum181837
Variance4.380012119
2020-11-20T09:58:01.044734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02366130.4%
 
21211815.5%
 
1905211.6%
 
5878511.3%
 
3871811.2%
 
6800910.3%
 
476049.8%
 
ValueCountFrequency (%) 
02366130.4%
 
1905211.6%
 
21211815.5%
 
3871811.2%
 
476049.8%
 
ValueCountFrequency (%) 
6800910.3%
 
5878511.3%
 
476049.8%
 
3871811.2%
 
21211815.5%
 

Interactions

2020-11-20T09:57:16.831851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:17.263815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:17.571811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:17.869812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:18.133811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:18.403966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:18.646793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:18.886831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:19.130836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:19.375963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:19.682517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:20.028081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:20.346002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:20.601003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:20.814034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:21.025034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:21.232034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:21.440260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:21.656258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:21.885310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:22.149837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:22.551884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:22.798481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:23.021517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:23.241515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:23.469481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:23.678481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:23.885481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:24.095482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:24.306482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:24.529505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:24.797507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:25.079111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:25.321595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:25.565596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:25.787596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:26.001596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:26.223635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:26.423597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:26.622596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:26.830599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:27.073600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:27.344600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:27.593958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:27.834954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:28.050956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:28.288959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:28.508960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:28.730971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:28.960977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:29.181973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:29.567975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:29.827977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:30.096976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:30.344975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:30.571973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:30.790973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:30.998007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:31.223972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:31.431976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:31.647973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:31.898975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:32.170975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:32.475976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:32.763975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:33.054976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:33.333974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:33.620978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:33.897976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:34.163978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:34.460979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:34.821977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:35.175975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:35.506979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:35.780976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:36.051980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:36.328978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:36.566975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:36.792979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:37.139992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:37.484497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:37.960076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:38.242075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:38.507083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:38.795077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:39.106137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:39.382134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:39.696699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:40.055219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:40.375262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:40.671773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:40.950771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:41.231771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:41.496794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:41.796401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:42.141368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:42.469888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:42.805469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:43.080980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:43.334531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:43.582497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:43.848502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:44.100497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:44.364092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:44.664667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:44.948193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:45.239192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:45.473252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:45.701016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:45.968641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:46.358642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:46.588606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:46.834608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:47.134688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:47.444234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:47.724199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:48.020236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:48.277535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:48.552677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:48.799274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:49.037792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:49.270776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:49.480810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:49.731773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:49.997780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:50.267774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:50.509335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:50.739842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:50.968847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:51.182864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:51.415380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:51.630384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:51.850404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:52.142405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:52.428936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:52.691486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:52.934048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:53.203049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:53.633049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:53.908102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:54.190589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:54.420590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:54.739151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:55.057154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-20T09:58:01.193766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-20T09:58:01.633996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-20T09:58:02.108622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-20T09:58:02.686196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-20T09:57:55.499206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:57:56.095241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

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9939225215433335892990453000510660154495400015687520014490454040105485650None0

Last rows

SekundeActiveCPU CyclesKernel Stacktemp s0energy s0TempS1energy s1memfreeBranch InstructionsXmit DataRunning AppRunning App ID
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779387793842266873011651230016520008452786643254000436850901319046020463743980None0
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77944779444216001593915194298405200084949431332540004408072351790456564108881060None0
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